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Parameters Estimation of Regression Model Based on the Improved AFSA

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Recent Developments in Intelligent Systems and Interactive Applications (IISA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 541))

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Abstract

This paper aims at improving the AFSA algorithm. The improved AFSA algorithm is applied on estimation of parameters for the multiple linear regression models. Comparing the AFSA and the Least Squares, the results of simulation experiments verify that the estimating performance of the improved algorithm is better than the AFSA and the Least Squares. Thus, a noble approach for estimation of parameters is proposed in this research work.

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References

  1. Yu, X.-L., Ren, X.-S.: Multivariate Statistical Analysis, 2nd edn. Statistics Press, London (2006). pp. 20–25

    Google Scholar 

  2. Liu, J.-P., Yu, J.-X.: Parameter estimation of multiple linear regression models based on the improved particle swarm optimization algorithm. Comput. Eng. Sci. 32(4), 101–105 (2011)

    Google Scholar 

  3. Qian, H., Li, S.-Q., Wang, W.-Y.: The study on determination of multivariate nolinear regression coefficient about genetic algorithm. J. Agric. Univ. 25(2), 07–14 (2002). Hebei

    Google Scholar 

  4. Zhang, J.-L.: Application of artificial bee colony algorithm to multiple linear regression. J. Guangdong Polytech. Normal Univ. 31(3), 31–33 (2011)

    Google Scholar 

  5. Sun, H., Zhang, Z.-M., Ge, H.-J.: Application of PSO to improve multiple linear regression. Comput. Eng. Appl. 43(3), 43–44 (2007)

    Google Scholar 

  6. Li, X.-L., Shao, Z.-J., Qian, J.: An optimizing method based on autonomous animals: fish-swarm algorithm. Syst. Eng. Theor. Pract. 22(11), 76–82 (2002)

    Google Scholar 

  7. Jiang, M.Y., Mastorakis, N.E., et al.: Multi-threshold image segmentation with improved artificial fish swarm algorithm. In: Proceedings of European Computing Conference (ECC 2007), pp. 117–120 (2007)

    Google Scholar 

  8. Chen, F.: Analysis and reaearch on improved Artificial Fish Swarm Algorithm. Xidian University, Xi’an (2012)

    Google Scholar 

  9. Xiao, J.M., Zheng, X.M., Wang, X.H., et al.: A modified artificial fish-swarm algorithm. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, pp. 3456–3460 (2006)

    Google Scholar 

  10. Li, Y.: Solution to problems concerning AFSA based multiple linear regression analysis. J. Bohai Univ. 32(2), 168–171 (2011). (Natural Science Edition)

    MATH  Google Scholar 

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Acknowledgment

The research was supported by National Natural Science Foundation of China. (Grant Nos. 11571138).

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Authors and Affiliations

  1. School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China

    Yujia Jin, Zhuoxi Yu & Milan Parmar

  2. Jilin Province Key Laboratory of Internet Finance, Changchun, China

    Yujia Jin & Zhuoxi Yu

Authors
  1. Yujia Jin
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  2. Zhuoxi Yu
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  3. Milan Parmar
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Corresponding author

Correspondence to Zhuoxi Yu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

  2. SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  3. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

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Jin, Y., Yu, Z., Parmar, M. (2017). Parameters Estimation of Regression Model Based on the Improved AFSA. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-49568-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49567-5

  • Online ISBN: 978-3-319-49568-2

  • eBook Packages: EngineeringEngineering (R0)

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